Impact
If you can measure it,
you can fix it.
The same belief that drives every consulting engagement — that transformation without measurement is guessing — applies to everything Christine works on. Measure what’s moving. See what’s working. Don’t stop until you can prove the needle is moving, not just assume it will.
01
AI Transformation Consulting
What it looks like when AI
proficiency actually moves.
Measured Results
Before. After.
In their own words.
Each participant completed a 25-dimension AI proficiency assessment before and after the program — covering prompting, workflow design, agent creation, team enablement, and strategic AI leadership. The percentage below reflects their improvement across those dimensions.
Sales & Business Development
+158%
After
“I'm using AI to research target companies and identify the right people for prospecting. I feel 10 out of 10 ready to lead this on my team.”
Healthcare Professional
+76%
After
“I'm using AI to research new illnesses, build care pathways, and surface what's working around the world. I feel 10 out of 10 ready.”
Operations & Planning
+64%
After
“With the classes I've taken, I can point people to the right places to begin. I'm ready.”
“My ability to utilize AI is an advantage as I look for new roles. I'm not afraid of it — it's a differentiator.”
“I can show coworkers how to use AI for their jobs. I can help people who are nervous about AI feel more confident.”
“I no longer worry about being replaced. I've figured out which parts of my job AI can help with — and which parts only I can do.”
02
The Phoenix Formula
AI in practice.
Career transformation
as a proof of concept.
Before Christine brought her measurement discipline to organizations, she tested it in the hardest possible domain: career coaching for professionals who’d been laid off. People months into a job search, savings running thin, rejection stacking up. Not a skills problem — a belief system problem.
She built specialized AI agents — one for each role in the job search. A resume writer. An interview coach. A LinkedIn strategist. A mindset coach. Not one agent doing everything, but the best possible person for each piece of the work. Because ten thousand hours makes you a master in one thing — and a job search needs a whole team.
But before she built anything, she solved the measurement problem first. She voice-memoed her root cause analysis — not a document, a real account of how she thinks about transformation — and fed it to AI as a measurement expert, using Douglas Hubbard’s framework from How to Measure Anything. The result: an assessment built from her own judgment. Inputs she could track and adjust.
The pre/post results were measured before a single participant had gotten a job. That was the point. Measure what leads to the outcome, track whether it’s moving, and keep adjusting until you know it’s working — not just hope the outcome follows.
Pre/Post Results — Measured Before Anyone Got a Job
300%
improvement in LinkedIn proficiency
150%
improvement in articulating unique value
75%
reduction in burnout
80%
reduction in sleep disruption
+28%
improvement in confidence — zero declines
67%
reduction in feeling completely lost or defeated
Access the Phoenix Formula
The Phoenix Formula is a full AI agent system for professionals rebuilding after a layoff. It’s pro bono — free to anyone who needs it. If you or someone you know is in the middle of a job search, reach out.
Get Access →03
Foster Care Reform
The kids are failing.
The data proves it.
No one’s measuring.
There is no consistent state performing well across all outcome areas in foster care. States are working in silos. The programs that receive funding have no real measurement requirements. The kids — not attending school, lacking basic support, placed in group homes run on profit margins instead of outcomes — are failing in ways that are visible and preventable.
Christine mentors a teenager in foster care, one-on-one, pro bono. What she sees up close is what the data confirms at scale: the same system failures repeating, in every state, with no accountability for whether any of it actually works.
She is building toward a different approach — one that brings the same measurement discipline, design thinking, and AI-enabled capability building she uses with organizations into the foster care reform space. Define the inputs that actually predict good outcomes for children. Measure them. Track what’s moving. Hold programs accountable to the data, not just the funding. Don’t stop until you know it’s working.
What the System Is Missing
No outcome measurement.
Programs receive public funding with no requirement to demonstrate results. There is no standard for what good looks like.
States working in silos.
No consistent state is performing well across all areas. No data sharing. No cross-state learning. The same failures repeat.
Group homes run on profit margins.
Profitability is the metric. Outcomes for kids are not. Understaffing and overcrowding are the predictable result.
Kids not in school.
Instability of placement disrupts education. No one is accountable for continuity. Children fall through the gaps of a system that wasn't designed to hold them.
No consistent care.
Too many moves, too many strangers, too little support. The lack of continuity compounds every other problem.
The same belief.
Three different arenas.
Whether it’s an organization adopting AI, a professional rebuilding after a layoff, or a system failing the most vulnerable kids — the discipline is the same. Define the inputs that actually predict the outcome you want. Measure whether they’re moving. See what’s working and what isn’t. Keep adjusting until you can prove it, not just hope for it.
Transformation without measurement is guessing. And guessing wastes time, money, and in some cases, people’s lives.
This work — in all three arenas — is in progress. If you work in child welfare, policy, or philanthropic investment and want to think about the foster care piece together, reach out.